Sandeep Chawda
@famt.ac.in
Associate Professor
Finolex Academy of Management and Technology, Ratnagiri
RESEARCH INTERESTS
Restructured Power Systems, Power System Economics, Electricity Markets, Renewable Energy, Energy Trading
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Sandeep Chawda and Vivek Prakash
Springer Science and Business Media LLC
Sandeep Chawda, Parul Mathuria, and Rohit Bhakar
Springer Science and Business Media LLC
Swati Gupta, Bharat Bhushan Sharma, Vivek Prakash, Sandeep Chawda, and Kailash Chand Sharma
IEEE
The major power share from uncertain wind power producers would create a formidable challenge for grid management. Uncertain generation characteristics may impact the trading strategy of power producers. Hence, there is a need to model involved uncertainties for accuracy in the trading decision in an electricity market. The randomness of wind speed could be described by a stochastic process and characterized via probabilistic scenarios. However, probabilistic scenario-based models are computationally demanding and require a large number of scenarios. In this regard, this paper presents the modified interval forecast approach along with the stochastic scenario approach. In the modified interval approach uncertain interval is forecasted as upper and lower bounds and hourly ramps are considered using net-load scenarios. The proposed modified interval model results in computationally fast solutions and is accurate to capture available wind power output. Proposed models would be useful for wind power producers in trading decision-making.
Sandeep Chawda, Parul Mathuria, and Rohit Bhakar
Springer Science and Business Media LLC
Sandeep Chawda, Parul Mathuria, and Rohit Bhakar
Elsevier BV
S Sreekumar, R Bhakar, S Chawda, A Jain, and V Prakash
IEEE
Power transaction process are competitive in restructured markets. Market operations such as bidding, hedging, planning of facility investments and negotiation of bilateral contracts are rely on electricity price. Electricity price uncertainty is the major barrier in profit maximization of market participants. This price uncertainty can be addressed by using accurate forecasts in market operations. Artificial intelligence and time series-based models are used for day ahead electricity price forecasting. Such models necessitate massive training inputs for accurate forecasting, which leads to high processing time. However, fast and accurate forecasts are required for real time decision making in market operations. Therefore, this paper proposes three support vector regression forecasting models for day ahead electricity price forecasting. Proposed models use limited quantum of training inputs as they use similar day approach for forecasting. Also, proposed model use GA and PSO based hyper parameter optimization for improving forecasting accuracy. Data collected from Indian power market is used for performance evaluation. Performance of proposed models are compared with linear regression and artificial neural network models. Results show that proposed models have very strong potential towards day ahead electricity price forecasting.
Sandeep Chawda, Parul Mathuria, Rohit Bhakar, and Sreenu Sreekumar
IEEE
This paper presents an optimization model to determine optimal dynamic sale prices and optimal energy procurement decisions of a load-serving entity (LSE) in the presence of solar energy for consumer’s elastic demand. LSE determines these decisions to maximize its profit at a given level of risk. Consumer’s elastic demand is considered by its price elasticity, and price uncertainty is modeled using the mean-variance approach. Optimal decisions of risk-neutral and risk-averse LSE are illustrated through a case study. Results indicate that LSE builds consumer demand by lowering sale prices during solar energy availability hours to utilize it optimally.
S Sreekumar, K C Sharma, R Bhakar, S Chawda, F Teotia, and V Prakash
IEEE
Stochastic nature of wind power generation creates techno-economic challenges to power system operations. Ancillary services such as operational reserves are used to address such challenges. Ancillary services increase the operating cost of utility companies and increased operating cost is suggested to be paid by Wind Power Producers (WPP). Penalizing wind power deviation from the forecast is one of the effective ways to shift increased operating costs from utility companies to WPPs. WPPs profit is getting reduced due to high deviation charges. Deviation charges can be reduced by enhancing wind power forecasting accuracy. Several models such as statistical methods and artificial neural networks are available for wind power forecasting. However, there is a scope of accuracy improvement, because forecasting errors result in significant techno-economic challenges. Also, existing forecasting approaches focus on error matrices rather than economic factors such as deviation charges. Expressing forecasting performance in terms of money will motivate WPPs to improve the forecasting accuracy to reduce deviation charges. The deviation charge estimation of an individual plant is obsolete due to the occurrence of negatively correlated forecasting errors of different plants. Therefore, this paper proposes a novel Support Vector Regression (SVR) based aggregated wind forecasting model for deviation charge reduction. SVR models are widely used in the last few years, due to its superior performance over ANN and statistical methods. Hyper-parameters of proposed SVR model is intelligently tuned using Particle Swarm Optimization (PSO) to ensure optimum model performance. Results show that the proposed model has low deviation charges compared to reference models.
Sandeep Chawda, Parul Mathuria, and Rohit Bhakar
Hindawi Limited
Sandeep Chawda, Parul Mathuria, and Rohit Bhakar
IEEE
Renewable energy (RE) penetration has strongly increased in the electricity market worldwide. Variability and availability of RE are challenging load-serving entity’s (LSE) decision-making about energy procurement portfolio and retail price offer. The influence of RE penetration on LSE’s optimal decisions would increase with penetration level. This necessitates an investigation on LSE’s participation strategy in electricity market under significant RE penetration. Besides RE, consumers’ flexible demand may negatively affect LSE’s decisions and eventually its expected profit. This necessitates dynamic retail pricing to manage flexible demand and exploit monetary benefits from it. In this perspective, this paper presents a model for LSE to determine optimal dynamic retail prices and optimal energy procurement portfolio in the presence of flexible demand and significant RE penetration. A case study is employed to illustrate and validate the proposed model.
Falti Teotia, Parul Mathuria, Rohit Bhakar, Vivek Prakash, and Sandeep Chawda
IEEE
To encourage transaction of electricity within distribution network for satisfying the demand locally under intermittency of distributed energy resources, Local Electricity Market (LEM) can be considered as a solution which encourage direct participation of end users in trading by incentivizing prosumers locally. The consumers also get benefited by low cost power available from neighbor-hood prosumers. In this context, this paper proposes a LEM model and trading framework using system constraints. The outcomes of financial market clearing are used for assessing power flows in a MV level network. The analysis is done to highlight the mutual impact of network constraints on market clearing, and LEM on power flows and congestion. The simulation results highlight the need to couple both network operation and market operations for successful implementation of LEM into the existing system.
Kailash Chand Sharma, Rohit Bhakar, H. P. Tiwari, and Sandeep Chawda
IEEE
Energy trading in liberalized electricity markets is a decision-making problem that is modeled considering price uncertainty. Stochastic programming is a natural platform for modeling such decision-making problems, where uncertainties are characterized through scenarios. Scenarios are possible outcomes of random process with corresponding occurrence probabilities. A large number of scenarios are required for accurate modeling of any uncertainty. However, due to computational complexity and time limitations, generated scenarios are required to be reduced. This paper presents a efficacious algorithm for generation and reduction of electricity market price scenarios. Time series based Auto Regressive Moving Average (ARMA) model is used for scenario generation while Probability Distance Based Backward reduction method is utilized for scenario reduction. Proposed algorithm is illustrated through practical case study based on PJM day-ahead electricity market. Statistical analysis validates the proposed algorithm and comparison between ARMA and heuristic model for scenario generation reflect strength of proposed algorithm for modeling electricity market price uncertainty.
Sandeep Chawda, Parul Mathuria, Rohit Bhakar, Sreenu Sreekumar, Vivek Prakash, and Falti Teotia
IEEE
This work addresses the problem of an electricity retailer who manages two sides of trading; optimal procurement of electricity from wholesale market to minimize the procurement cost and optimal selling price calculation considering a price responsive demand, to maximize revenue. On the procurement side, retailer faces uncertainty of pool electricity price and at sell side, it sets selling price based on the elasticity of its consumer demand. The work contributes by highlighting the impact of demand behavior on a retailer's decision making for different price elasticity and providing informed decision support under pool price uncertainty to address different risk-averse nature of retailers.
V. Prakash, K. C. Sharma, R. Bhakar, H. P. Tiwari, S. Sreekumar, S. Chawda, and F. Teotia
IEEE
Bulk integration of uncertain PV generation is likely to pose real-time operational challenges for the system operators to provide primary frequency response (PFR). PFR requirement for frequency stability has been recognized widely and its impact on the generation scheduling needs indepth investigation. This necessitates optimized PFR from generation scheduling and quantification of associated uncertainty. In this perspective, this paper put forward a novel stochastic scheduling model for operation and PFR cost minimization, with optimized PFR under PV generation uncertainty. Moreover, the impact of frequency response parameter variation on operation cost and PV generation curtailment is investigated. Case studies are carried out on one area IEEE RTS and the proposed method is compared with the conventional deterministic unit commitment (DUC). Results show the efficacy of the proposed method in terms of optimized PFR, operation cost reduction, PFR cost, PV curtailment and potential to address uncertainties for low carbon systems.
Sandeep Chawda, Rohit Bhakar, and Parul Mathuria
IEEE
During last two decades, power industry underwent major changes all over the world. One is regulatory reform towards distributed and competitive structure, in which market forces drive the electricity prices, and the other is a large share of renewable in total electricity generation mix. These bring in unpredictable future conditions i e. uncertainty in prices and volume of electricity at the time of planning. These significantly changed the traditional working of various entities in the power sector and raised many challenges and opportunities. This paper briefs some of the challenges and opportunities to major entities (generation companies, retailers, large consumer and system operators) of restructured power sector and highlights the necessity of uncertainty consideration and its various impacts from the perspective of different entities.